Beyond the KV cache: Training and serving i14’s Laplace 3B linear attention model on AWS Trainium2 with native PyTorch

Extracto de la fuente

Most large language models (LLMs) use transformer architectures with softmax attention. During inference, their key-value (KV) cache grows with the conversation or document context. This can reduce concurrency, increase serving cost, and force agentic applications to discard useful history. i14 designed Laplace 3B as a linear attention alternative. Its recurrent state stays at 29.5 MB per sequence, independent of context length. Working with AWS, i14 used native PyTorch support through…